

import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


lowercase			:				Union[str, Any]   =     get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
		lowercase			:				str   =     json.load(f)







@require_torch
class 					__lowercase  (						unittest.TestCase				):





							"""simple docstring"""

							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       List[str]:
														return FSMTTokenizer.from_pretrained(__UpperCAmelCase						)




							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       Dict:
														A      :					List[Any]      		=					FSMTForConditionalGeneration.from_pretrained(__UpperCAmelCase						).to(__UpperCAmelCase						)
														if torch_device == "cuda":
																					model.half()
														return model






							@parameterized.expand(
							    [
							        ['''en-ru''', 2_6.0],
							        ['''ru-en''', 2_2.0],
							        ['''en-de''', 2_2.0],
							        ['''de-en''', 2_9.0],
							    ]						)
							@slow
							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase						)		->       Union[str, Any]:
														# note: this test is not testing the best performance since it only evals a small batch
														# but it should be enough to detect a regression in the output quality
														A      :					Dict      		=					f'facebook/wmt19-{pair}'
														A      :					Tuple      		=					self.get_tokenizer(__UpperCAmelCase						)
														A      :					List[Any]      		=					self.get_model(__UpperCAmelCase						)

														A      :					Tuple      		=					bleu_data[pair]['''src''']
														A      :					Optional[Any]      		=					bleu_data[pair]['''tgt''']

														A      :					str      		=					tokenizer(__UpperCAmelCase    ,						return_tensors='''pt'''    ,						truncation=__UpperCAmelCase    ,						padding='''longest'''						).to(__UpperCAmelCase						)
														A      :					str      		=					model.generate(
														    input_ids=batch.input_ids    ,						num_beams=8    ,						)
														A      :					Union[str, Any]      		=					tokenizer.batch_decode(
														    __UpperCAmelCase    ,						skip_special_tokens=__UpperCAmelCase    ,						clean_up_tokenization_spaces=__UpperCAmelCase						)
														A      :					Optional[int]      		=					calculate_bleu(__UpperCAmelCase    ,						__UpperCAmelCase						)
														print(__UpperCAmelCase						)
														self.assertGreaterEqual(scores['''bleu''']    ,						__UpperCAmelCase						)









from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_ = "x"     ,				lowerCamelCase_ = 10**-10     ,				lowerCamelCase_ = 1     ,				):
							A      :					int      		=					symbols(lowerCamelCase_		)
							A      :					List[str]      		=					lambdify(lowerCamelCase_     ,				lowerCamelCase_		)
							A      :					Tuple      		=					lambdify(lowerCamelCase_     ,				diff(lowerCamelCase_     ,				lowerCamelCase_		)		)

							A      :					Optional[int]      		=					starting_point

							while True:
														if diff_function(lowerCamelCase_		) != 0:
																					A      :					Union[str, Any]      		=					prev_guess - multiplicity * func(lowerCamelCase_		) / diff_function(
																					    lowerCamelCase_		)
														else:
																					raise ZeroDivisionError('''Could not find root'''		) from None

														# Precision is checked by comparing the difference of consecutive guesses
														if abs(next_guess - prev_guess		) < precision:
																					return next_guess

														A      :					Tuple      		=					next_guess


# Let's Execute
if __name__ == "__main__":
		# Find root of trigonometric function
		# Find value of pi
		print(F"The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}")

		# Find root of polynomial
		# Find fourth Root of 5
		print(F"The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5J)}")

		# Find value of e
		print(
		    "The root of log(y) - 1 = 0 is ",
		    F"{newton_raphson('log(y) - 1', 2, variable='y')}",
		)

		# Exponential Roots
		print(
		    "The root of exp(x) - 1 = 0 is",
		    F"{newton_raphson('exp(x) - 1', 10, precision=0.0_05)}",
		)

		# Find root of cos(x)
		print(F"The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}")






